Mobile edge computation rate maximization method based on semi-supervised learning

Abstract

The invention discloses a mobile edge computation rate maximization method based on semi-supervised learning. The method comprises the following steps: 1) each set of wireless equipment needs to establish contact with a base station; 2) two non-overlapping sets M0 and M1 are used for respectively presenting all wireless equipment at the local computing mode and the shunting mode; 3) the wireless equipment in the set M0 can collect energy and simultaneously process the local task, and the wireless equipment in the set M1 can shunt the task to the base station to process after collecting the energy; and 4) the mode selection of all wireless equipment is determined through the own channel gain hi, the effect of the semi-supervised learning comprises taking the channel gain as the input, thereby generating an optimal mode selection capable of maximizing the sum computing rate of all wireless equipment, namely, deciding the task of which wireless equipment is processed at the local and which is shunt to the base station to process. In the premise of guaranteeing the user experience, the sum computation rate of all wireless equipment is maximized.

Description

technical field [0001] The invention belongs to the field of communication, and in particular relates to a communication system for mobile edge computing and a method for maximizing the rate of mobile edge computing based on semi-supervised learning. Background technique [0002] Recent developments in IoT technology are a critical step towards truly intelligent and autonomous control, especially in many important industrial and commercial systems. In an IoT network, a large number of wireless devices (WDs) capable of communication and computing are deployed. Due to device size constraints and production cost considerations, IoT devices (such as sensors) often carry batteries with limited capacity and energy-saving low Performance processors, therefore, limited device lifetime and low computing power cannot support the growing number of sustainable new applications requiring high-performance computing, such as autonomous driving and augmented reality. The deployment of wire...

AI Technical Summary

Problems solved by technology

In an IoT network, a large number of wireless devices (WDs) capable of communication and computing are deployed. Due to device size constraints and production cost considerations, IoT devices (such as sensors) often carry batteries with limited capacity and energy-saving low Performance processors, therefore, limited device lifetime and low computing power cannot support the growing number of sustainable new applications requiring high-performance computing, such as autonomous driving and augmented reality

The deployment of wireless power transfer systems (WPT) can solve the two aforementioned performance problems, but frequent device battery failures not only disrupt the normal operation of individual wireless devices but also significantly degrade the overall network performance, for example, in wireless sensor networks Sensing accuracy

Traditional wireless systems require frequent manual battery replacement, which is expensive and inconvenient. Due to strict battery capacity constraints, in battery-powered wireless systems, minimizing energy consumption and prolonging the operating life cycle of wireless devices is a key design

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